Research Internship On ai-driven Field Boundary Detection: Leveraging Satellite Imagery to Support Digital Agriculture Adoption

Le descriptif de l’offre ci-dessous est en Anglais_ **Type de contrat**: Stage **Niveau de diplôme exigé**: Bac + 4 ou équivalent **Fonction**: Stagiaire de la recherche **A propos du centre ou de la direction fonctionnelle**: The Inria centre at Université Côte d'Azur includes 42 research teams and 9 support services. The centre's staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM...), but also with the regiona economic players. With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels. **Contexte et atouts du poste**: The team, located in a multidisciplinary laboratory, has an active and stimulating environment with master, PhD and Post-doc students coming from different countries. **Mission confiée**: Join our dynamic research team and you'll work on Transformer-based models to **automatically extract agricultural field boundaries from satellite imagery [2]**. **What You'll Work On**: Depending on your interests, you'll explore one of two research tracks: **1) Transformer + Geometric Priors for Curvilinear Structures** Leverage the recent **Time Series Vision Transformers (TsViT)** model [1] in combination with **procedural geometric priors,**such as curvilinear structures [3], to accurately detect and model field boundaries—integrating both temporal and spatial insights to guide geometric extraction. **2) Foundation Models + Graph Neural Networks for End-to-End Vectorization** Combine a **Semantic Segmentation Foundation Model (e.g., SAM2 [4])** with **Graph Neural Networks** to directly extract and vectorize agricultural fields in a single step—bridging raw data to precise geometric output [5,6]. **What You'll Gain**: - Hands-on experience with **state-of-the-art AI models** for geospatial analysis - The opportunity to **co-author a scientific publication**, depending on your progress - A collaborative and supportive environment at the forefront of **AI for Earth observation** [1] Tarasiou, Michail, Erik Chavez, and Stefanos Zafeiriou. "Vits for sits: Vision transformers for satellite image time series." _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_. 2023. [2] Kerner, Hannah, et al. "Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation." _arXiv preprint arXiv:2409.16252_ (2024). [3] Cheng, Mingfei, et al. "Joint topology-preserving and feature-refinement network for curvilinear structure segmentation." _Proceedings of the IEEE/CVF International Conference on Computer Vision_. 2021. [4] Ravi, Nikhila, et al. "Sam 2: Segment anything in images and videos." _arXiv preprint arXiv:2408.00714_ (2024). [5] Hetang, Congrui, et al. "Segment anything model for road network graph extraction." _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_. 2024. [6] Adimoolam, Yeshwanth Kumar, Charalambos Poullis, and Melinos Averkiou. "Pix2poly: A sequence prediction method for end-to-end polygonal building footprint extraction from remote sensing imagery." _2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)_. IEEE, 2025. **Principales activités**: The selected intern will be expected to: - **Review and analyze relevant scientific literature** in deep learning and geospatial data processing. - **Design a method** to incorporate geometric prior knowledge into Transformer-based models or combine multiple models to extract and vectorize geometrical objects. - **Implement the proposed approach** using the PyTorch framework. - **Conduct experiments and benchmark performance** using real-world datasets for agricultural field boundary detection. - **Document and communicate findings**, including clear summaries, visualizations, and evaluations of the results. **Compétences**: **Technical Skills - Required Level** - **Advanced Python programming** - Strong proficiency with **data manipulation libraries** (e.g., NumPy, Pandas) - Expertise in **deep learning frameworks** such as **PyTorch** (preferred) or **TensorFlow** - Experience with **image analysis techniques**, including segmentation, object detection, and classification - Familiarity with **satellite or remote sensing data** is a plus **Avantages**: - Subsidized meals - Partial reimbursement of public transport costs - Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in wor